Cancer is the second leading cause of death in the United States. There is an acute need for cancer biomarkers that can be detected from clinically relevant samples and used for early diagnosis, therapeutic follow-up, and prognosis of malignant diseases. Proteins are principal regulators and effectors of physiologic and pathophysiologic processes. As such, proteomics is expected to play a role in clinical biomarker discovery.
Clinical proteomics is an emerging area of proteome research. For example, to identify cancer biomarkers by analyzing clinically relevant specimens routinely procured from an individual/subject. The ultimate goal of clinical oncoproteomics is to characterize proteins within a tumor environment, as well as peripheral biofluid(s) in specimens obtained from a newly diagnosed cancer subject. Individualized approaches to cancer management demand detailed and robust analyses of that subject's tumor phenotype, for necessary insights to what has been deranged (i.e., key signaling pathways, essential molecular elements). So far, the translation of proteomic assays to applicable diagnostic and/or prognostic tests in clinical oncology has been disappointing.
A number of factors currently hinder MS-based cancer biomarker research/discovery. For example, heterogeneity or variations in specific disease/cancer processes as well as the human population (in general) may introduce biases into complex proteomic datasets hindering subsequent bioinformatic and statistical analyses. Moreover, according to findings reported by the Human Proteome Organization, the dynamic range of the human plasma protein concentration is in the order of 1010 while the dynamic range of contemporary MS instrumentation is 104 at best resulting in a formidable mismatch between the dynamic range of MS instrumentation and the dynamic range of human specimens. Additionally, the majority of the MS-proteomic derived “potential” cancer biomarkers are not directly germane to the tumor under study. Many of these proteins fall into the categories of acute-phase reactants and are not specific to the patho-biology under study. Moreover, some cancers may secrete a protein that is detectable within a fluid sample, while others may only manifest themselves by markers that are detectable within (or in the immediate vicinity of) the tumor. Thus, one type of sample method cannot be used to detect all types of cancer. Therefore, a need exists for oncoproteomic methodologies to rectify these issues and facilitate proteomic profiling of clinical specimens in the context of personalized medicine.